{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Translating_from_English_to_German.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.11"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xb3Rg99_Khi_"
      },
      "source": [
        "This is entirely based on Ben Trevett's notebook on transformers for translation: https://github.com/bentrevett/pytorch-seq2seq\n",
        "\n",
        "All credit goes to him."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tj_p94cNEsnq",
        "outputId": "1ee66995-7918-45bd-921f-eca93daa5347",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        }
      },
      "source": [
        "# on colab you might have to restart you notebook after executing this:\n",
        "!pip install 'torchtext==0.7.0'"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting torchtext==0.7.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b9/f9/224b3893ab11d83d47fde357a7dcc75f00ba219f34f3d15e06fe4cb62e05/torchtext-0.7.0-cp36-cp36m-manylinux1_x86_64.whl (4.5MB)\n",
            "\u001b[K     |████████████████████████████████| 4.5MB 4.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchtext==0.7.0) (1.18.5)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from torchtext==0.7.0) (1.6.0+cu101)\n",
            "Collecting sentencepiece\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e5/2d/6d4ca4bef9a67070fa1cac508606328329152b1df10bdf31fb6e4e727894/sentencepiece-0.1.94-cp36-cp36m-manylinux2014_x86_64.whl (1.1MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1MB 55.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from torchtext==0.7.0) (4.41.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from torchtext==0.7.0) (2.23.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch->torchtext==0.7.0) (0.16.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->torchtext==0.7.0) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->torchtext==0.7.0) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->torchtext==0.7.0) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->torchtext==0.7.0) (2020.6.20)\n",
            "Installing collected packages: sentencepiece, torchtext\n",
            "  Found existing installation: torchtext 0.3.1\n",
            "    Uninstalling torchtext-0.3.1:\n",
            "      Successfully uninstalled torchtext-0.3.1\n",
            "Successfully installed sentencepiece-0.1.94 torchtext-0.7.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "torchtext"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6FooXlPbLuoV"
      },
      "source": [
        "You should make sure you are running this in a GPU runtime."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6XrNYyjxLt8y",
        "outputId": "517a326b-a720-435a-b42e-2d39da7a032d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sun Aug 16 17:01:54 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 450.57       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   56C    P8    11W /  70W |      0MiB / 15079MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9Ub9tAhKhjA"
      },
      "source": [
        "## Preparing the Data\n",
        "\n",
        "Let's import all the required modules upfront"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PzPIP1ZjKhjB"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "import torchtext\n",
        "from torchtext.datasets import Multi30k\n",
        "from torchtext.data import Field, BucketIterator\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker\n",
        "\n",
        "import spacy\n",
        "import numpy as np\n",
        "\n",
        "import random\n",
        "import math\n",
        "import time"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5GJ4ZVGZKhjE"
      },
      "source": [
        "# we can set the random seeds for reproducability\n",
        "SEED = 1234\n",
        "\n",
        "random.seed(SEED)\n",
        "np.random.seed(SEED)\n",
        "torch.manual_seed(SEED)\n",
        "torch.cuda.manual_seed(SEED)\n",
        "torch.backends.cudnn.deterministic = True"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Up40Dz9KhjH"
      },
      "source": [
        "We'll then create our tokenizers as before."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xouwq0oQKz-G",
        "outputId": "1122baf0-1390-4723-b615-84a975350528",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "!python -m spacy download de"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: de_core_news_sm==2.2.5 from https://github.com/explosion/spacy-models/releases/download/de_core_news_sm-2.2.5/de_core_news_sm-2.2.5.tar.gz#egg=de_core_news_sm==2.2.5 in /usr/local/lib/python3.6/dist-packages (2.2.5)\n",
            "Requirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.6/dist-packages (from de_core_news_sm==2.2.5) (2.2.4)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.18.5)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.0.2)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.0.0)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.0.2)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (2.23.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (49.2.0)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (2.0.3)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (3.0.2)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (7.4.0)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.1.3)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (0.4.1)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (0.7.1)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (4.41.1)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->de_core_news_sm==2.2.5) (1.7.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->de_core_news_sm==2.2.5) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->de_core_news_sm==2.2.5) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->de_core_news_sm==2.2.5) (2020.6.20)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->de_core_news_sm==2.2.5) (1.24.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->de_core_news_sm==2.2.5) (3.1.0)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('de_core_news_sm')\n",
            "\u001b[38;5;2m✔ Linking successful\u001b[0m\n",
            "/usr/local/lib/python3.6/dist-packages/de_core_news_sm -->\n",
            "/usr/local/lib/python3.6/dist-packages/spacy/data/de\n",
            "You can now load the model via spacy.load('de')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7sc9LVzpKhjI"
      },
      "source": [
        "spacy_de = spacy.load('de')\n",
        "spacy_en = spacy.load('en')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xhCYiYL8KhjK"
      },
      "source": [
        "def tokenize_de(text):\n",
        "    \"\"\"\n",
        "    Tokenizes German text from a string into a list of strings\n",
        "    \"\"\"\n",
        "    return [tok.text for tok in spacy_de.tokenizer(text)]\n",
        "\n",
        "def tokenize_en(text):\n",
        "    \"\"\"\n",
        "    Tokenizes English text from a string into a list of strings\n",
        "    \"\"\"\n",
        "    return [tok.text for tok in spacy_en.tokenizer(text)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qiVCMM9KKhjN"
      },
      "source": [
        "Our fields are the same as the previous notebook. The model expects data to be fed in with the batch dimension first, so we use `batch_first = True`. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Xw_ZHXHKhjN",
        "outputId": "37c7a27e-8b94-4181-db33-662d08cadcb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "SRC = Field(\n",
        "    tokenize=tokenize_en, \n",
        "    init_token='<sos>', \n",
        "    eos_token='<eos>', \n",
        "    lower=True, \n",
        "    batch_first=True\n",
        ")\n",
        "\n",
        "TRG = Field(\n",
        "    tokenize=tokenize_de, \n",
        "    init_token='<sos>', \n",
        "    eos_token='<eos>', \n",
        "    lower=True, \n",
        "    batch_first=True\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchtext/data/field.py:150: UserWarning: Field class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.\n",
            "  warnings.warn('{} class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.'.format(self.__class__.__name__), UserWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Eg6JMC5KhjQ"
      },
      "source": [
        "We then load the Multi30k dataset and build the vocabulary."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2raG3Kc-KhjQ",
        "outputId": "2dc0cbd5-b8f6-41c2-82ac-84e3ec6882fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "train_data, valid_data, test_data = Multi30k.splits(\n",
        "    exts=('.en', '.de'), \n",
        "    fields=(SRC, TRG)\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchtext/data/example.py:78: UserWarning: Example class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.\n",
            "  warnings.warn('Example class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.', UserWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kh2HPBVRKhjU"
      },
      "source": [
        "SRC.build_vocab(train_data, min_freq=2)\n",
        "TRG.build_vocab(train_data, min_freq=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ohZj4h7YKhjX"
      },
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wknMn_DzL9Jt"
      },
      "source": [
        "assert device.__str__() == 'cuda'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WbaKahF5Khja",
        "outputId": "55cb3bd3-539d-4304-edcf-61cc87cedbd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "BATCH_SIZE = 128\n",
        "\n",
        "train_iterator, valid_iterator, test_iterator = BucketIterator.splits(\n",
        "    (train_data, valid_data, test_data), \n",
        "     batch_size=BATCH_SIZE,\n",
        "     device=device\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchtext/data/iterator.py:48: UserWarning: BucketIterator class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.\n",
            "  warnings.warn('{} class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.'.format(self.__class__.__name__), UserWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_sAD4u9jKhjc"
      },
      "source": [
        "class Encoder(nn.Module):\n",
        "    def __init__(self,\n",
        "                 input_dim,\n",
        "                 hid_dim,\n",
        "                 n_layers,\n",
        "                 n_heads,\n",
        "                 pf_dim,\n",
        "                 dropout,\n",
        "                 device,\n",
        "                 max_length=100):\n",
        "        super().__init__()\n",
        "        self.device = device\n",
        "        self.tok_embedding = nn.Embedding(input_dim, hid_dim)\n",
        "        self.pos_embedding = nn.Embedding(max_length, hid_dim)\n",
        "        self.layers = nn.ModuleList(\n",
        "            [EncoderLayer(\n",
        "                hid_dim,\n",
        "                n_heads,\n",
        "                pf_dim,\n",
        "                dropout,\n",
        "                device\n",
        "            ) for _ in range(n_layers)])\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)\n",
        "        \n",
        "    def forward(self, src, src_mask):\n",
        "        batch_size = src.shape[0]\n",
        "        src_len = src.shape[1]\n",
        "        pos = torch.arange(\n",
        "            0, src_len\n",
        "        ).unsqueeze(0).repeat(\n",
        "            batch_size, 1\n",
        "        ).to(self.device)\n",
        "        src = self.dropout(\n",
        "            (self.tok_embedding(src) * self.scale) +\n",
        "            self.pos_embedding(pos)\n",
        "        )\n",
        "        for layer in self.layers:\n",
        "            src = layer(src, src_mask)\n",
        "        return src"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "89I1RDSjKhjf"
      },
      "source": [
        "class EncoderLayer(nn.Module):\n",
        "    def __init__(self,\n",
        "                 hid_dim,\n",
        "                 n_heads,\n",
        "                 pf_dim,\n",
        "                 dropout,\n",
        "                 device):\n",
        "        super().__init__()\n",
        "        self.self_attn_layer_norm = nn.LayerNorm(hid_dim)\n",
        "        self.ff_layer_norm = nn.LayerNorm(hid_dim)\n",
        "        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)\n",
        "        self.positionwise_feedforward = PositionwiseFeedforwardLayer(\n",
        "            hid_dim, pf_dim, dropout\n",
        "        )\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "    def forward(self, src, src_mask):\n",
        "        _src, _ = self.self_attention(src, src, src, src_mask)\n",
        "        src = self.self_attn_layer_norm(src + self.dropout(_src))\n",
        "        _src = self.positionwise_feedforward(src)\n",
        "        src = self.ff_layer_norm(src + self.dropout(_src))\n",
        "        return src"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tEALF4PGKhji"
      },
      "source": [
        "class MultiHeadAttentionLayer(nn.Module):\n",
        "    def __init__(self, hid_dim, n_heads, dropout, device):\n",
        "        super().__init__()\n",
        "        assert hid_dim % n_heads == 0\n",
        "        self.hid_dim = hid_dim\n",
        "        self.n_heads = n_heads\n",
        "        self.head_dim = hid_dim // n_heads\n",
        "        self.fc_q = nn.Linear(hid_dim, hid_dim)\n",
        "        self.fc_k = nn.Linear(hid_dim, hid_dim)\n",
        "        self.fc_v = nn.Linear(hid_dim, hid_dim)\n",
        "        self.fc_o = nn.Linear(hid_dim, hid_dim)\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "        self.scale = torch.sqrt(torch.FloatTensor([self.head_dim])).to(device)\n",
        "\n",
        "    def forward(self, query, key, value, mask = None):\n",
        "        batch_size = query.shape[0]\n",
        "        Q = self.fc_q(query)\n",
        "        K = self.fc_k(key)\n",
        "        V = self.fc_v(value)\n",
        "        Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
        "        K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
        "        V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
        "        energy = torch.matmul(Q, K.permute(0, 1, 3, 2)) / self.scale\n",
        "        if mask is not None:\n",
        "            energy = energy.masked_fill(mask == 0, -1e10)\n",
        "        attention = torch.softmax(energy, dim = -1)\n",
        "        x = torch.matmul(self.dropout(attention), V)\n",
        "        x = x.permute(0, 2, 1, 3).contiguous()\n",
        "        x = x.view(batch_size, -1, self.hid_dim)\n",
        "        x = self.fc_o(x)\n",
        "        return x, attention"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yjvHux8mKhjk"
      },
      "source": [
        "class PositionwiseFeedforwardLayer(nn.Module):\n",
        "    def __init__(self, hid_dim, pf_dim, dropout):\n",
        "        super().__init__()\n",
        "        self.fc_1 = nn.Linear(hid_dim, pf_dim)\n",
        "        self.fc_2 = nn.Linear(pf_dim, hid_dim)\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.dropout(torch.relu(self.fc_1(x)))\n",
        "        x = self.fc_2(x)\n",
        "        return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XKIowBoSKhjn"
      },
      "source": [
        "class Decoder(nn.Module):\n",
        "    def __init__(self,\n",
        "                 output_dim,\n",
        "                 hid_dim,\n",
        "                 n_layers,\n",
        "                 n_heads,\n",
        "                 pf_dim,\n",
        "                 dropout,\n",
        "                 device,\n",
        "                 max_length=100):\n",
        "        super().__init__()\n",
        "        self.device = device\n",
        "        self.tok_embedding = nn.Embedding(\n",
        "            output_dim, hid_dim\n",
        "        )\n",
        "        self.pos_embedding = nn.Embedding(\n",
        "            max_length, hid_dim\n",
        "        )\n",
        "        self.layers = nn.ModuleList(\n",
        "            [DecoderLayer(\n",
        "                hid_dim,\n",
        "                n_heads,\n",
        "                pf_dim,\n",
        "                dropout,\n",
        "                device\n",
        "            ) for _ in range(n_layers)]\n",
        "        )\n",
        "        self.fc_out = nn.Linear(hid_dim, output_dim)\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)\n",
        "\n",
        "    def forward(self, trg, enc_src, trg_mask, src_mask):\n",
        "        batch_size = trg.shape[0]\n",
        "        trg_len = trg.shape[1]\n",
        "        pos = torch.arange(\n",
        "            0,\n",
        "            trg_len).unsqueeze(0).repeat(\n",
        "            batch_size,\n",
        "            1).to(\n",
        "            self.device)\n",
        "        trg = self.dropout(\n",
        "            (self.tok_embedding(trg) *\n",
        "             self.scale) +\n",
        "            self.pos_embedding(pos))\n",
        "        for layer in self.layers:\n",
        "            trg, attention = layer(trg, enc_src, trg_mask, src_mask)\n",
        "        output = self.fc_out(trg)\n",
        "        return output, attention"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "viLqLvX7Khjr"
      },
      "source": [
        "class DecoderLayer(nn.Module):\n",
        "    def __init__(self,\n",
        "                 hid_dim,\n",
        "                 n_heads,\n",
        "                 pf_dim,\n",
        "                 dropout,\n",
        "                 device):\n",
        "        super().__init__()\n",
        "        self.self_attn_layer_norm = nn.LayerNorm(hid_dim)\n",
        "        self.enc_attn_layer_norm = nn.LayerNorm(hid_dim)\n",
        "        self.ff_layer_norm = nn.LayerNorm(hid_dim)\n",
        "        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)\n",
        "        self.encoder_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)\n",
        "        self.positionwise_feedforward = PositionwiseFeedforwardLayer(\n",
        "            hid_dim, pf_dim, dropout\n",
        "        )\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "    def forward(self, trg, enc_src, trg_mask, src_mask):\n",
        "        _trg, _ = self.self_attention(trg, trg, trg, trg_mask)\n",
        "        trg = self.self_attn_layer_norm(trg + self.dropout(_trg))\n",
        "        _trg, attention = self.encoder_attention(trg, enc_src, enc_src, src_mask)\n",
        "        trg = self.enc_attn_layer_norm(trg + self.dropout(_trg))\n",
        "        _trg = self.positionwise_feedforward(trg)\n",
        "        trg = self.ff_layer_norm(trg + self.dropout(_trg))\n",
        "        return trg, attention"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "daOoQhSTKhjv"
      },
      "source": [
        "class Seq2Seq(nn.Module):\n",
        "    def __init__(self,\n",
        "                 encoder,\n",
        "                 decoder,\n",
        "                 src_pad_idx,\n",
        "                 trg_pad_idx,\n",
        "                 device):\n",
        "        super().__init__()\n",
        "        self.encoder = encoder\n",
        "        self.decoder = decoder\n",
        "        self.src_pad_idx = src_pad_idx\n",
        "        self.trg_pad_idx = trg_pad_idx\n",
        "        self.device = device\n",
        "\n",
        "    def make_src_mask(self, src):\n",
        "        src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)\n",
        "        return src_mask\n",
        "\n",
        "    def make_trg_mask(self, trg):\n",
        "        trg_pad_mask = (trg != self.trg_pad_idx).unsqueeze(1).unsqueeze(2)\n",
        "        trg_len = trg.shape[1]\n",
        "        trg_sub_mask = torch.tril(torch.ones((trg_len, trg_len), device=self.device)).bool()\n",
        "        trg_mask = trg_pad_mask & trg_sub_mask\n",
        "        return trg_mask\n",
        "\n",
        "    def forward(self, src, trg):\n",
        "        src_mask = self.make_src_mask(src)\n",
        "        trg_mask = self.make_trg_mask(trg)\n",
        "        enc_src = self.encoder(src, src_mask)\n",
        "        output, attention = self.decoder(trg, enc_src, trg_mask, src_mask)\n",
        "        return output, attention"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVISPwMsKhjx"
      },
      "source": [
        "## Training the Seq2Seq Model\n",
        "\n",
        "We can now define our encoder and decoders. This model is significantly smaller than Transformers used in research today, but is able to be run on a single GPU quickly."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVXraJ3SKhjx"
      },
      "source": [
        "INPUT_DIM = len(SRC.vocab)\n",
        "OUTPUT_DIM = len(TRG.vocab)\n",
        "HID_DIM = 256\n",
        "ENC_LAYERS = 3\n",
        "DEC_LAYERS = 3\n",
        "ENC_HEADS = 8\n",
        "DEC_HEADS = 8\n",
        "ENC_PF_DIM = 512\n",
        "DEC_PF_DIM = 512\n",
        "ENC_DROPOUT = 0.1\n",
        "DEC_DROPOUT = 0.1\n",
        "\n",
        "enc = Encoder(\n",
        "    INPUT_DIM, \n",
        "    HID_DIM, \n",
        "    ENC_LAYERS, \n",
        "    ENC_HEADS, \n",
        "    ENC_PF_DIM, \n",
        "    ENC_DROPOUT, \n",
        "    device\n",
        ")\n",
        "\n",
        "dec = Decoder(\n",
        "    OUTPUT_DIM, \n",
        "    HID_DIM, \n",
        "    DEC_LAYERS, \n",
        "    DEC_HEADS, \n",
        "    DEC_PF_DIM, \n",
        "    DEC_DROPOUT, \n",
        "    device\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Nk7Ty2GKhj0"
      },
      "source": [
        "Then, use them to define our whole sequence-to-sequence encapsulating model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "smseAOAwKhj0"
      },
      "source": [
        "SRC_PAD_IDX = SRC.vocab.stoi[SRC.pad_token]\n",
        "TRG_PAD_IDX = TRG.vocab.stoi[TRG.pad_token]\n",
        "\n",
        "model = Seq2Seq(enc, dec, SRC_PAD_IDX, TRG_PAD_IDX, device).to(device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aLLms5UoKhj2"
      },
      "source": [
        "We can check the number of parameters, noticing it is significantly less than the 37M for the convolutional sequence-to-sequence model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mWohDQGlKhj2",
        "outputId": "b0bcb66d-8811-43e9-fdad-b9734c8bad69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "def count_parameters(model):\n",
        "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "\n",
        "print(f'The model has {count_parameters(model):,} trainable parameters')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The model has 9,543,087 trainable parameters\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a9ygCZ0WKhj6"
      },
      "source": [
        "def initialize_weights(m):\n",
        "    if hasattr(m, 'weight') and m.weight.dim() > 1:\n",
        "        nn.init.xavier_uniform_(m.weight.data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZhB5c5ynKhj8"
      },
      "source": [
        "model.apply(initialize_weights);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fhUabkKOKhj_"
      },
      "source": [
        "LEARNING_RATE = 0.0005\n",
        "\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ec_jBKWzKhkC"
      },
      "source": [
        "Next, we define our loss function, making sure to ignore losses calculated over `<pad>` tokens."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVkMzCMMKhkD"
      },
      "source": [
        "criterion = nn.CrossEntropyLoss(ignore_index=TRG_PAD_IDX)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LfDgtL7ZKhkG"
      },
      "source": [
        "def train(model, iterator, optimizer, criterion, clip):\n",
        "    model.train()\n",
        "    epoch_loss = 0\n",
        "    for i, batch in enumerate(iterator):\n",
        "        src = batch.src\n",
        "        trg = batch.trg\n",
        "        optimizer.zero_grad()\n",
        "        output, _ = model(src, trg[:, :-1])\n",
        "        output_dim = output.shape[-1]\n",
        "        output = output.contiguous().view(-1, output_dim)\n",
        "        trg = trg[:,1:].contiguous().view(-1)\n",
        "        loss = criterion(output, trg)\n",
        "        loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), clip)\n",
        "        optimizer.step()\n",
        "        epoch_loss += loss.item()\n",
        "    return epoch_loss / len(iterator)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vfYkc7IsKhkI"
      },
      "source": [
        "The evaluation loop is the same as the training loop, just without the gradient calculations and parameter updates."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-kwTnYVKhkI"
      },
      "source": [
        "def evaluate(model, iterator, criterion):\n",
        "    model.eval()\n",
        "    epoch_loss = 0\n",
        "    with torch.no_grad():\n",
        "        for i, batch in enumerate(iterator):\n",
        "            src = batch.src\n",
        "            trg = batch.trg\n",
        "            output, _ = model(src, trg[:, :-1])\n",
        "            output_dim = output.shape[-1]\n",
        "            output = output.contiguous().view(-1, output_dim)\n",
        "            trg = trg[:,1:].contiguous().view(-1)\n",
        "            loss = criterion(output, trg)\n",
        "            epoch_loss += loss.item()\n",
        "    return epoch_loss / len(iterator)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I2Nhx6gPKhkL"
      },
      "source": [
        "We then define a small function that we can use to tell us how long an epoch takes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g7L8OtisKhkL"
      },
      "source": [
        "def epoch_time(start_time, end_time):\n",
        "    elapsed_time = end_time - start_time\n",
        "    elapsed_mins = int(elapsed_time / 60)\n",
        "    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
        "    return elapsed_mins, elapsed_secs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OhlJ7qHkKhkP"
      },
      "source": [
        "Finally, we train our actual model. This model is almost 3x faster than the convolutional sequence-to-sequence model and also achieves a lower validation perplexity!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fAkLLMWvKhkP",
        "outputId": "2020569f-d578-481b-9462-41dd11a99050",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        }
      },
      "source": [
        "N_EPOCHS = 10\n",
        "CLIP = 1\n",
        "\n",
        "best_valid_loss = float('inf')\n",
        "\n",
        "for epoch in range(N_EPOCHS):\n",
        "    \n",
        "    start_time = time.time()\n",
        "    \n",
        "    train_loss = train(model, train_iterator, optimizer, criterion, CLIP)\n",
        "    valid_loss = evaluate(model, valid_iterator, criterion)\n",
        "    \n",
        "    end_time = time.time()\n",
        "    \n",
        "    epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
        "    \n",
        "    if valid_loss < best_valid_loss:\n",
        "        best_valid_loss = valid_loss\n",
        "        torch.save(model.state_dict(), 'en2de-model.pt')\n",
        "    \n",
        "    print(f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s')\n",
        "    print(f'\\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}')\n",
        "    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. PPL: {math.exp(valid_loss):7.3f}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchtext/data/batch.py:23: UserWarning: Batch class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.\n",
            "  warnings.warn('{} class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.'.format(self.__class__.__name__), UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 01 | Time: 0m 20s\n",
            "\tTrain Loss: 0.786 | Train PPL:   2.194\n",
            "\t Val. Loss: 1.495 |  Val. PPL:   4.459\n",
            "Epoch: 02 | Time: 0m 20s\n",
            "\tTrain Loss: 0.717 | Train PPL:   2.048\n",
            "\t Val. Loss: 1.527 |  Val. PPL:   4.602\n",
            "Epoch: 03 | Time: 0m 20s\n",
            "\tTrain Loss: 0.655 | Train PPL:   1.924\n",
            "\t Val. Loss: 1.548 |  Val. PPL:   4.704\n",
            "Epoch: 04 | Time: 0m 20s\n",
            "\tTrain Loss: 0.604 | Train PPL:   1.829\n",
            "\t Val. Loss: 1.568 |  Val. PPL:   4.798\n",
            "Epoch: 05 | Time: 0m 20s\n",
            "\tTrain Loss: 0.557 | Train PPL:   1.745\n",
            "\t Val. Loss: 1.579 |  Val. PPL:   4.849\n",
            "Epoch: 06 | Time: 0m 20s\n",
            "\tTrain Loss: 0.517 | Train PPL:   1.677\n",
            "\t Val. Loss: 1.632 |  Val. PPL:   5.114\n",
            "Epoch: 07 | Time: 0m 20s\n",
            "\tTrain Loss: 0.484 | Train PPL:   1.622\n",
            "\t Val. Loss: 1.681 |  Val. PPL:   5.369\n",
            "Epoch: 08 | Time: 0m 20s\n",
            "\tTrain Loss: 0.451 | Train PPL:   1.569\n",
            "\t Val. Loss: 1.689 |  Val. PPL:   5.415\n",
            "Epoch: 09 | Time: 0m 20s\n",
            "\tTrain Loss: 0.423 | Train PPL:   1.526\n",
            "\t Val. Loss: 1.697 |  Val. PPL:   5.457\n",
            "Epoch: 10 | Time: 0m 20s\n",
            "\tTrain Loss: 0.397 | Train PPL:   1.487\n",
            "\t Val. Loss: 1.746 |  Val. PPL:   5.734\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y-xPdezRKhkR"
      },
      "source": [
        "We load our \"best\" parameters and manage to achieve a better test perplexity than all previous models."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uO7KJxp7KhkS",
        "outputId": "a2b3593c-dcca-4a08-9263-5c900bc546b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "model.load_state_dict(torch.load('en2de-model.pt'))\n",
        "\n",
        "test_loss = evaluate(model, test_iterator, criterion)\n",
        "\n",
        "print(f'| Test Loss: {test_loss:.3f} | Test PPL: {math.exp(test_loss):7.3f} |')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "| Test Loss: 1.519 | Test PPL:   4.566 |\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchtext/data/batch.py:23: UserWarning: Batch class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.\n",
            "  warnings.warn('{} class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.'.format(self.__class__.__name__), UserWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LXt-_3oHKhkU"
      },
      "source": [
        "## Inference\n",
        "\n",
        "Now we can can translations from our model with the `translate_sentence` function below.\n",
        "\n",
        "The steps taken are:\n",
        "- tokenize the source sentence if it has not been tokenized (is a string)\n",
        "- append the `<sos>` and `<eos>` tokens\n",
        "- numericalize the source sentence\n",
        "- convert it to a tensor and add a batch dimension\n",
        "- create the source sentence mask\n",
        "- feed the source sentence and mask into the encoder\n",
        "- create a list to hold the output sentence, initialized with an `<sos>` token\n",
        "- while we have not hit a maximum length\n",
        "  - convert the current output sentence prediction into a tensor with a batch dimension\n",
        "  - create a target sentence mask\n",
        "  - place the current output, encoder output and both masks into the decoder\n",
        "  - get next output token prediction from decoder along with attention\n",
        "  - add prediction to current output sentence prediction\n",
        "  - break if the prediction was an `<eos>` token\n",
        "- convert the output sentence from indexes to tokens\n",
        "- return the output sentence (with the `<sos>` token removed) and the attention from the last layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFKYR0oqKhkU"
      },
      "source": [
        "def translate_sentence(sentence, src_field, trg_field, model, device, max_len=50):\n",
        "    model.eval()\n",
        "    if isinstance(sentence, str):\n",
        "        nlp = spacy.load('en')\n",
        "        tokens = [token.text.lower() for token in nlp(sentence)]\n",
        "    else:\n",
        "        tokens = [token.lower() for token in sentence]\n",
        "    tokens = [src_field.init_token] + tokens + [src_field.eos_token]\n",
        "    src_indexes = [src_field.vocab.stoi[token] for token in tokens]\n",
        "    src_tensor = torch.LongTensor(src_indexes).unsqueeze(0).to(device)\n",
        "    src_mask = model.make_src_mask(src_tensor)\n",
        "    with torch.no_grad():\n",
        "        enc_src = model.encoder(src_tensor, src_mask)\n",
        "    trg_indexes = [trg_field.vocab.stoi[trg_field.init_token]]\n",
        "    for i in range(max_len):\n",
        "        trg_tensor = torch.LongTensor(trg_indexes).unsqueeze(0).to(device)\n",
        "        trg_mask = model.make_trg_mask(trg_tensor)\n",
        "        with torch.no_grad():\n",
        "            output, attention = model.decoder(trg_tensor, enc_src, trg_mask, src_mask)\n",
        "        pred_token = output.argmax(2)[:, -1].item()\n",
        "        trg_indexes.append(pred_token)\n",
        "        if pred_token == trg_field.vocab.stoi[trg_field.eos_token]:\n",
        "            break\n",
        "    trg_tokens = [trg_field.vocab.itos[i] for i in trg_indexes]\n",
        "    return trg_tokens[1:], attention"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pRa9LmDrKhkW"
      },
      "source": [
        "We'll now define a function that displays the attention over the source sentence for each step of the decoding. As this model has 8 heads our model we can view the attention for each of the heads."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqt4kiojKhkW"
      },
      "source": [
        "def display_attention(sentence, translation, attention, n_heads = 8, n_rows = 4, n_cols = 2):\n",
        "    \n",
        "    assert n_rows * n_cols == n_heads\n",
        "    \n",
        "    fig = plt.figure(figsize=(15,25))\n",
        "    \n",
        "    for i in range(n_heads):\n",
        "        \n",
        "        ax = fig.add_subplot(n_rows, n_cols, i+1)\n",
        "        \n",
        "        _attention = attention.squeeze(0)[i].cpu().detach().numpy()\n",
        "\n",
        "        cax = ax.matshow(_attention, cmap='bone')\n",
        "\n",
        "        ax.tick_params(labelsize=12)\n",
        "        ax.set_xticklabels(['']+['<sos>']+[t.lower() for t in sentence]+['<eos>'], \n",
        "                           rotation=45)\n",
        "        ax.set_yticklabels(['']+translation)\n",
        "\n",
        "        ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
        "        ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
        "\n",
        "    plt.show()\n",
        "    plt.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pXAPj4YtKhkY"
      },
      "source": [
        "First, we'll get an example from the training set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lY7trVz2KhkZ",
        "outputId": "f0c7198a-264f-4176-ab83-bfe7b6dfd509",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "example_idx = 8\n",
        "\n",
        "src = vars(train_data.examples[example_idx])['src']\n",
        "trg = vars(train_data.examples[example_idx])['trg']\n",
        "\n",
        "print(f'src = {src}')\n",
        "print(f'trg = {trg}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "src = ['a', 'woman', 'with', 'a', 'large', 'purse', 'is', 'walking', 'by', 'a', 'gate', '.']\n",
            "trg = ['eine', 'frau', 'mit', 'einer', 'großen', 'geldbörse', 'geht', 'an', 'einem', 'tor', 'vorbei', '.']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cW1VU-QeKhkb"
      },
      "source": [
        "Our translation looks actually better than the original translation. A purse is not really a wallet (geldbörse), but a small bag (handtasche)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5OO_jCKZKhkb",
        "outputId": "574796cc-2754-47d9-cb8e-f8ff08f3ba85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "translation, attention = translate_sentence(src, SRC, TRG, model, device)\n",
        "\n",
        "print(f'predicted trg = {translation}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "predicted trg = ['eine', 'frau', 'mit', 'einer', 'großen', 'handtasche', 'geht', 'an', 'einem', 'tor', 'vorbei', '.', '<eos>']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tK8JhhxZKhkd"
      },
      "source": [
        "We can see the attention from each head below. Each is certainly different, but it's difficult (perhaps impossible) to reason about what head has actually learned to pay attention to. Some heads pay full attention to \"eine\" when translating \"a\", some don't at all, and some do a little. They all seem to follow the similar \"downward staircase\" pattern and the attention when outputting the last two tokens is equally spread over the final two tokens in the input sentence."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YKjntgQDKhkd",
        "outputId": "45821a1d-e146-4723-b0f7-af4d137ede91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "display_attention(src, translation, attention)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1800 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ieIpv-CKhkf"
      },
      "source": [
        "Next, let's get an example the model has not been trained on from the validation set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAPJLRSsKhkg",
        "outputId": "9860a686-5183-4bb1-9ce8-119b5261476a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "example_idx = 6\n",
        "\n",
        "src = vars(valid_data.examples[example_idx])['src']\n",
        "trg = vars(valid_data.examples[example_idx])['trg']\n",
        "\n",
        "print(f'src = {src}')\n",
        "print(f'trg = {trg}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "src = ['a', 'brown', 'dog', 'is', 'running', 'after', 'the', 'black', 'dog', '.']\n",
            "trg = ['ein', 'brauner', 'hund', 'rennt', 'dem', 'schwarzen', 'hund', 'hinterher', '.']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hMTOpKcwKhkh"
      },
      "source": [
        "The model translates it by switching *is running* to just *running*, but it is an acceptable swap."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ts9Gm-ImKhki",
        "outputId": "b62fcf01-7859-4271-bcbd-18304d22c3c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "translation, attention = translate_sentence(src, SRC, TRG, model, device)\n",
        "\n",
        "print(f'predicted trg = {translation}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "predicted trg = ['ein', 'brauner', 'hund', 'rennt', 'nach', 'dem', 'schwarzen', 'hund', '.', '<eos>']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NNxcU0rOKhkl"
      },
      "source": [
        "Again, some heads pay full attention to \"ein\" whilst some pay no attention to it. Again, most of the heads seem to spread their attention over both the period and `<eos>` tokens in the source sentence when outputting the period and `<eos>` sentence in the predicted target sentence, though some seem to pay attention to tokens from near the start of the sentence."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mwc4vJ3iKhkl",
        "outputId": "241c99d6-ea8b-48ec-fccd-04f4ce82d0ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "display_attention(src, translation, attention)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1800 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aI45cHbPKhkp"
      },
      "source": [
        "Finally, we'll look at an example from the test data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JogMjZOVKhkq",
        "outputId": "4f9db87f-33d5-4dd3-c78a-4a9933324957",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "example_idx = 10\n",
        "\n",
        "src = vars(test_data.examples[example_idx])['src']\n",
        "trg = vars(test_data.examples[example_idx])['trg']\n",
        "\n",
        "print(f'src = {src}')\n",
        "print(f'trg = {trg}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "src = ['a', 'mother', 'and', 'her', 'young', 'song', 'enjoying', 'a', 'beautiful', 'day', 'outside', '.']\n",
            "trg = ['eine', 'mutter', 'und', 'ihr', 'kleiner', 'sohn', 'genießen', 'einen', 'schönen', 'tag', 'im', 'freien', '.']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWK1_y2GKhkt"
      },
      "source": [
        "A decent translation with *young* being omitted."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0UVI5ttaKhku",
        "outputId": "461fdf79-09bf-44e7-df7a-c1bee9f061ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "translation, attention = translate_sentence(src, SRC, TRG, model, device)\n",
        "\n",
        "print(f'predicted trg = {translation}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "predicted trg = ['eine', 'mutter', 'und', 'ihre', 'kleine', 'jungen', 'genießen', 'ein', '<unk>', 'im', 'freien', '.', '<eos>']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y-k3JbadKhkw",
        "outputId": "8fe09adf-a1ec-4467-e80c-1ae6f8415777",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "display_attention(src, translation, attention)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1800 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DbhdgGTnKhk1"
      },
      "source": [
        "## BLEU\n",
        "\n",
        "Finally we calculate the BLEU score for the Transformer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dX589bvWKhk1"
      },
      "source": [
        "from torchtext.data.metrics import bleu_score  # works in torchtext-0.7.0\n",
        "\n",
        "def calculate_bleu(data, src_field, trg_field, model, device, max_len=50):\n",
        "    trgs = []\n",
        "    pred_trgs = []\n",
        "    \n",
        "    for datum in data:\n",
        "        src = vars(datum)['src']\n",
        "        trg = vars(datum)['trg']\n",
        "        pred_trg, _ = translate_sentence(src, src_field, trg_field, model, device, max_len)\n",
        "        \n",
        "        #cut off <eos> token\n",
        "        pred_trg = pred_trg[:-1]\n",
        "        \n",
        "        pred_trgs.append(pred_trg)\n",
        "        trgs.append([trg])\n",
        "\n",
        "    return bleu_score(pred_trgs, trgs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQI45GzBKhk3"
      },
      "source": [
        "We get a BLEU score of 33.57, which is not bad, while training fewer parameters and the fastest training time!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fE19f7dBKhk4",
        "outputId": "ea5e34f9-5e66-4648-c9f1-6d7a1af55720",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "bleu_score = calculate_bleu(test_data, SRC, TRG, model, device)\n",
        "\n",
        "print(f'BLEU score = {bleu_score*100:.2f}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BLEU score = 33.57\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bTU80Od0Khk7"
      },
      "source": [
        "Congratulations for finishing these tutorials! I hope you've found them useful.\n",
        "\n",
        "If you find any mistakes or want to ask any questions about any of the code or explanations used, feel free to submit a GitHub issue and I will try to correct it ASAP. "
      ]
    }
  ]
}